- (3/8/2021) Initial release
- (3/30/2021) New color detection method (Accuracy Increased ~30%)
-
Clone this repository:
git clone --recurse-submodules https://github.com/lpcvai/21LPCVC-UAV_VIdeo_Track-Sample-Solution.git cd 21LPCVC-UAV_VIdeo_Track-Sample-Solution -
Create a virtual environment and install it's dependencies:
conda env create -f environment.yml conda activate SampleSolution
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The trained weights are provided here. The weights are called
best.ptand should be placed underyolov5/weights/. Make sure to use them to get the best detections. The trained weights were created using a dataset containing over 12,000 images. More stats on the dataset can be found inyolov5/weights/stats.txt. Specific stats about the training session can be viewed here if you're interested. -
The DeepSORT weights need to be downloaded; they can be found here. They should be called
ckpt.t7and place it underdeep_sort/deep_sort/deep/checkpoint/
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The first input will be a video. The sample videos can be located here.
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The second input is a csv file, containing the first 10 frames for the solution to acquire the correct labels. An additional 10 frames will be provided in the middle of the video, to recalibrate the labels if some identity switching occurs. The format for the input file in
inputs/"videoname".csvshould be similar to the example below. NOTE: The delimiter between each value in the actual csv file will be a comma (","), the | is just for visualization. The bounding box coordinate system is based off of the YOLO annotation format.
Frame | Class | ID | X | Y | Width | Height|
----------------------------------------------------------
0 | 0 | 1 |0.41015|0.39583|0.02031|0.03425|
0 | 0 | 2 |0.36835|0.61990|0.04557|0.18055|
0 | 1 | 3 |0.41015|0.39583|0.03593|0.16296|
1 | 0 | 1 |0.52942|0.39583|0.02031|0.03425|
1 | 0 | 2 |0.36835|0.61990|0.04557|0.18055|
1 | 1 | 3 |0.52942|0.39537|0.03593|0.16296|
- Frame: The frame number of the annotation
- Class: 0 for person, 1 for sports ball
- X = absolute_x / image_width
- Y = absolute_y / image_height
- Width = absolute_width /image_width
- Height = absolute_height /image_height
- The only output from the solution should be a text file. This text file will include the location of every ball when a single ball has been caught. The format for the output file in
outputs/"videoname"_out.csvshould be similar to the example below. NOTE: The delimiter between each value in the actual csv file will be a comma (","), the | is just for visualization.
Frame | Yellow | Orange | Red | Purple | Blue | Green |
----------------------------------------------------------
5 | 0 | 1 | 5 | 2 | 4 | 0 - Person 4 catches blue
30 | 0 | 3 | 5 | 2 | 4 | 0 - Person 3 catches orange
49 | 0 | 3 | 1 | 2 | 4 | 0 - Person 1 catches red
60 | 0 | 3 | 1 | 2 | 5 | 0 - Person 5 catches blue
python3 track.py --source VIDEOSOURCE --groundtruths PATHTOCSV --save-img
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